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        "author": "Artem Streltsov",
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        "description": "C-SGHMC\r\n=======================\r\n\r\nThis is a repository created as part of a statistical computation project at Duke University.\r\n\r\nThe code to be found here is a C++ implementation of SGHMC algorithm wrapped for Python using pybind11. It is further used to verify the results in the original paper on SGHMC: \"Stochastic Gradient Hamiltonian Monte Carlo\" by Tianqi Chen, Emily B. Fox and Carlos Guestrin.\r\n\r\nAs seen from our project this is a **15-time-efficiency-improvement** in terms of runtime over the pure python version.\r\n\r\nMain files\r\n=======================\r\n- **sghmcwrap.cpp** is the original sghmc algo in C++ wrapped for Python\r\n\r\n- **c_sghmc.py** calls and decorates the above extension and tests on an example from the original paper \r\nPackages required to run the code\r\n=======================\r\n\r\n- The implementation makes use of **Eigen** library for C++ and hence run the following line:\r\n! git clone https://github.com/RLovelett/eigen.git\r\n\r\nMake sure you have it cloned in the same directory as your code.\r\n\r\n- **pybind11** and **cppimport** are the two standard packages required for wrapping C++ codes for Python and importing them:\r\n! pip3 install pybind11\r\n\r\n! pip3 install cppimport\r\n\r\nThe example makes use of **sympy** to specify functions (such as potential and kinetic energies as well as the gradient of the former), **numpy** and **numpy.random**.\r\n",
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